We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
PP47 Modelling Non-small Cell Lung Cancer Treatment: Predicted and Observed Impact Of Immunotherapy In The Netherlands.
- Authors
Mfumbilwa, Zakile A; Wilschut, Janneke A; Simons, Martijn J.H.G; Ramaekers, Bram; Joore, Manuela; Retel, Valesca; Cramer-van der Welle, Christine M; Schramel, Franz M.N.H; van de Garde, Ewoudt M.W; Coupé, Veerle M.H
- Abstract
Introduction: Patients treated with immunotherapy are divided into two subgroups: (i) long-term survivors (LTS) and (ii) moderate survivors. Nevertheless, clinical trials (RCTs) report only average treatment effects such as hazard rate (HRs). Health economic-models often only input average treatment effects, even though it has been shown that accounting for the LTS subgroup is crucial for accurate projection of long-term survival under immunotherapy. We investigated the incorporation of a statistical mixture cure model (MCM) in a health-economic model for lung cancer as a way to account for LTS while incorporating reported average RCT-based treatment effects. Methods: We developed a microsimulation model describing disease progression under three treatment lines in advanced lung cancer using Dutch real-world data of chemotherapies treated patients. Here we focus on first-line treatment, for which we used gompertz distribution to simulate time-to-progression. To simulate the impact of immunotherapy, we adjusted base-model assuming MCM for first-line treatment, where the LTS subgroup was not at risk to progress, but instead die from background mortality. The subgroup of moderate survivors on the other hand are at risk to progress with adjusted progression-free HR (PF-HR). We simulated the model with size of LTS (prop_LTS) ranging from 14-34 percent (keynote-001 five-year overall survival [OS], 95% confidence interval) while fixing average RCT PF-HR at 0.5. Model predictions under the different prop_LTS were compared to real-world Dutch OS as well as the long-term RCT five-year OS. Results: With respect to observed short-term survival outcomes, model predictions were insensitive to assumptions regarding the size of the LTS subgroup. However, to match the five-year RCT OS rate reported (32%), the prop_LTS had to be equal to 34 percent. Under this latter setting for the prop_LTS, the progression HR in the subgroup of moderate survivors was calibrated to be 1.1. Conclusions: The use of a mixture cure model improves long-term model-based projections with the implicit assumption that moderate survivors have little or no treatment benefit.
- Subjects
NON-small-cell lung carcinoma; CANCER treatment; PROGRESSION-free survival; MICROSIMULATION modeling (Statistics); DISTRIBUTION (Probability theory); IMMUNOTHERAPY; TREATMENT effectiveness; PROGRAMMED cell death 1 receptors
- Publication
International Journal of Technology Assessment in Health Care, 2022, Vol 38, pS56
- ISSN
0266-4623
- Publication type
Abstract
- DOI
10.1017/S0266462322001878